Safety during operation of conventional industrial robots is usually accomplished with external sensor systems that are triggered by dangerous conditions and, when triggered, command the robot to shut down or work at a drastically reduced speed. Typically, the sensors are arranged to detect persons entering a work zone of the robot. For example, the sensor system may consist of or include pressure-sensitive mats on the floor, continuous beams of light that get broken by somebody walking through them, or a scanning laser range finder (including appropriate perceptual processing functionality) that detects movement of people. The drawback of such systems is that the presence of people near the robot necessarily causes a slow-down in, or even dead-stop of, the robot's work. The result is a significant efficiency decrease, which is exacerbated by the need to define a wide danger zone around the robot in order to avoid safety risks to people. This constraint also prevents people from working in close proximity to, e.g., collaboratively with, a robot.
One alternative to this all-or-nothing approach is to compute and predict trajectories of various body parts of human bystanders and various parts of the robot, anticipating possible collisions, assessing the potential damage that an impact between a particular body part with a particular robot part could cause, and basing a more refined safety decision on that knowledge for example, slowing the robot down to a speed commensurate with the computed risk rather than shutting the robot off altogether. This approach, however, is challenging to implement in practice given the complexity and inherent uncertainty of the computations: it is difficult to sense exactly where a person's body parts are and to predict where they are headed, and even more difficult to model human injury vulnerability for collisions between different parts of the human body and robot parts of different mechanical shape and hardnesses. Yet any compromises or uncertainties in modeling accuracy will either undermine the safety of the system or defeat efficiency gains relative to the all-or-nothing approach.
Accordingly, it is desirable to provide systems and methods for robot safety that are straightforwardly implemented while avoiding unnecessary interruptions and retardations of robot operation.